This notebook follows along the 3.4 Section produced for the publication “Comparing the density of mobile phone cell towers with population” part of the ONS Methodology Working Paper Series

The relationship between official estimates of UK residential and workday population1 density and the density of mobile phone cell-towers is investigated using freely available and open-sourced data on cell-tower locations from OpenCellID.

source("utils.R")

Using the cell-tower location measurements and the ggmap library2, a density estimation technique was used to make inferences about the underlying probability density functions.

In Kernel Density Estimation, the contribution of each data point is smoothed out from a single point into a region of space surrounding it. Aggregating the individually smoothed contributions gives an overall picture of the structure of the data and its density function.

ct = read.csv("data/London_ct.csv", header = T, sep = ",", stringsAsFactors = F)
head(ct)

Heathrow Airport

Although Heathrow Terminals 2 & 3 have a high density of cell towers, it is of note that higher densities are observed at major road junctions around the airport. This may be attributed to it being cheaper to site cell-towers on roads rather than in the airport. It may also be that larger cell-towers are allowed on roads than around the built environment, that there are very high numbers of people expected at these junctions, or possibly that OpenCellID receives GPS location information from travellers on these roads rather than in the airport itself.

draw_ggmap(location = "Heathrow Airport", zoom = 13, ct)
Source : https://maps.googleapis.com/maps/api/staticmap?center=Heathrow+Airport&zoom=13&size=640x640&scale=2&maptype=terrain&language=en-EN
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Heathrow%20Airport

London’s main stations

This technique was also applied to areas of London containing the major mainline stations: areas where high population densities would be expected during the day. The next plots reveal the high density of cell towers positioned in some of London’s main stations.

Waterloo Train Station

draw_ggmap(location = "Waterloo Train Station, London", zoom = 13, cell_towers)
Source : https://maps.googleapis.com/maps/api/staticmap?center=Waterloo+Train+Station,+London&zoom=13&size=640x640&scale=2&maptype=terrain&language=en-EN
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Waterloo%20Train%20Station%2C%20London

King’s Cross Train Station

draw_ggmap(location = "King's Cross Train Station, London", zoom = 15, cell_towers)
Source : https://maps.googleapis.com/maps/api/staticmap?center=King's+Cross+Train+Station,+London&zoom=15&size=640x640&scale=2&maptype=terrain&language=en-EN
Source : https://maps.googleapis.com/maps/api/geocode/json?address=King%27s%20Cross%20Train%20Station%2C%20London

Liverpool Street Train Station

draw_ggmap(location = "Liverpool Street Train Station, London", zoom = 15, cell_towers)
Source : https://maps.googleapis.com/maps/api/staticmap?center=Liverpool+Street+Train+Station,+London&zoom=15&size=640x640&scale=2&maptype=terrain&language=en-EN
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Liverpool%20Street%20Train%20Station%2C%20London

Victoria Train Station

draw_ggmap(location = "Victoria Train Station, London", zoom = 15, cell_towers)
Source : https://maps.googleapis.com/maps/api/staticmap?center=Victoria+Train+Station,+London&zoom=15&size=640x640&scale=2&maptype=terrain&language=en-EN
Source : https://maps.googleapis.com/maps/api/geocode/json?address=Victoria%20Train%20Station%2C%20London


1 Based on Census 2011

2 D. Kahle and H. Wickham. ggmap: Spatial Visualization with ggplot2. The R Journal, 5(1), 144-161. URL http://journal.r-project.org/archive/2013-1/kahle-wickham.pdf

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